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The Dynamic Range of Neonatal Heart Rate Variability
Author(s) -
GRIFFIN M. PAMELA,
SCOLLAN DAVID F.,
MOORMAN J. RANDALL
Publication year - 1994
Publication title -
journal of cardiovascular electrophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.193
H-Index - 138
eISSN - 1540-8167
pISSN - 1045-3873
DOI - 10.1111/j.1540-8167.1994.tb01151.x
Subject(s) - heart rate variability , medicine , heart rate , time domain , heart failure , cardiology , frequency domain , respiratory rate , blood pressure , mathematics , mathematical analysis , computer science , computer vision
Neonatal Heart Rate Variability. Introduction: Although it is generally appreciated that heart rate variability is low during severe illness, the extent, time course, and mathematical characteristics of heart rate variability during transitions between health and illness have not been systematically examined. The purpose of this study was to analyze heart rate variability in newborn infants during a rapid recovery from severe respiratory and circulatory failure. Methods and Results: From prolonged ECG recordings, we evaluated heart rate variability in the time domain (mean, relative change, and coefficient of variation of RR intervals), in the frequency domain (using power spectra of the time series of RR intervals), and using a neural network. Qualitatively, RR interval plots showed little heart rate variability during severe illness but became “noisier” during recovery. Quantitatively, recovery was marked by twofold to threefold increases in time‐domain parameters, by eightfold increases in frequency‐domain parameters, and by more than 20‐fold increases in a neural network measure. Time‐domain and frequency‐domain measures were correlated, but not strongly. Heart rate variability reached stable levels by 4 to 5 days. Heart rate did not change dramatically. Conclusion: Recovery from severe neonatal illness is accompanied by large and rapid increases in heart rate variability, but not by large changes in heart rate. This increase can be effectively assessed in the time domain, in the frequency domain, and by using a neural network.